README File associated with Replication Package for APSR Article State Legitimacy and Sector-Level Claim-Making: Evidence from East JerusalemDocuments included in replication package 1.	Replication data R script (file name=Replication Files_Early Bagdanov.R ) 2.	Analysis dataset for descriptive statistics, logistic regressions, and predicted probabilities (file name= Dataset_Early_Bagdanov.csv)3.	Raw dataset for descriptive statistics, logistic regressions, and predicted probabilities (file name= Raw_Early Bagdanov.csv)4.	Raw dataset for experiment (file name= exp_replicate.csv)5.	Shape file to generate map (file name= lamas_census_tracts_2011_jerusalem.shp) 6.	.shx file to generate the map (file name= lamas_census_tracts_2011_jerusalem.shx)7.	.dbf file to generate the map (file name= lamas_census_tracts_2011_jerusalem.dbf)8.	.prj file to generate the map (file name= lamas_census_tracts_2011_jerusalem.prj)9.	Supplemental variables .csv file to generate map (file name= maps_data.csv) 10.	Proof of IRB Approval for all protocols and amendments (file name= IRB Approval_All Protocols and Amendments_Early Bagdanov.pdf) 11.	Semi-structured interview question bank (file name= Semi-Structured Interview Question Bank_Early Bagdanov.pdf) 12.	Survey oral pre-script (file name= Survey Oral Script_Early Bagdanov.pdf) 13.	Survey question wording (file name= Survey Question Wording_Early Bagdanov.pdf)14.	Informed consent document for survey (file name=Informed Consent Survey_Early Bagdanov.pdf)15.	Informed consent document for interviews (file name= Informed Consent_Interviews_Early Bagdanov.pdf)  Instructions for replicating data 1.	To replicate descriptive statistics, logistic regressions, and predicted probabilities; a.	Load replication file in R, Replication Files_Early Bagdanov.Rb.	Load analysis data, Dataset_Early_Bagdanov.csv in R. c.	Load listed packages and run R script, which will output; i.	The percentages included in Table 3 column five ( �Percent Engage (n=1255)� column) from which the results of Table 3 column six were computed (�Percent abstain (n=1255)�),ii.	The results of the logistic regressions and associated regression output Tables 4 and 5,  iii.	The predicted probabilities and the associated Figure 32.	To replicate experimental results; a.	Load replication file in R, Replication Files_Early Bagdanov.Rb.	Load raw data associated with the experiment in R (file name= exp_replicate.csv)c.	Load listed additional packages and run R script, which will; i.	Take the raw data, which includes 7530 observations of the four-part survey experiment Question 29, and merge it to create a composite dependent variable (composite likability score) and associated attribute variable (treatments and controls), ii.	Relevel the treatment/control variable to assign treatment and control conditions, iii.	Generate the average treatment effects of each attribute, iv.	Cluster standard errors by subject number,v.	Create a dataframe to house the results, vi.	Generate Figure 4 and the associated regression output table (Table 3 included in the Supplementary Materials) 3.	To replicate Figure 1 map; a.	Download .shp, .shx, .dbf, and .prj files from Dataverse to your Desktop, which is the working directory as specified in line 28 of the R code b.	Load replication file in R, Replication Files_Early Bagdanov.R, c.	Load Shape file which contains outline of Jerusalem Municipality and neighborhoods, a.	Load supplemental variables .csv file to generate map (file name= maps_data.csv) d.	Once these files are loaded run the associated R script, which will; i.	Create a new dataframe binding together variables in the two datasetsii.	Generate the map in Figure 1 Description of how variables in raw dataset were converted to variables in analysis dataset; 1.	Region: The region variable is a nominal variable which records respondents� neighborhood of residence using numbers 1-19 that are associated with each of the 19 East Jerusalem neighborhoods. The associated Region variable in the raw dataset is not recoded and appears as-is in the analysis dataset also titled Region.2.	Age: The Age variable is computed using the Q2 variable in the raw dataset, which is a four-digit record of the respondent�s birth year. The Age variable is an ordinal variable that separates respondents into six age brackets (18-29, 30-39, 40-49, 50-59, 60-70, 70 and above) such that a 1 is associated with bracket 1 (18-29) and so forth.  3.	Edu_level: The Edu_level variable is an ordinal variable with seven levels which records the highest level of education of the respondent completed (No formal education; Elementary; Preparatory; Secondary; Mid-level diploma/professional or technical; BA; MA and above). A 1 is associated with No formal education and so forth. The associated Q22 variable in the raw dataset is not recoded and appears as-is in the analysis dataset titled as Edu_level.  4.	Relig_attend: The relig_attend variable is an ordinal variable recording the frequency with which individuals attend places of worship (Always, Most of the time, Sometimes, Rarely, Only on holidays, Never). The relig_attend variable in the analysis dataset is a merged version of two separate variables in the raw dataset, Q20 and Q21, which ask about religious attendance to Friday Prayers for Muslim respondents and Sunday services for Christian respondents respectively. The order of the numbers is reversed in the analysis dataset, such that when a respondent answers �Always� this is coded as a 6, �Most of the time� is coded as a 5, �Sometimes� is coded as a 4, �Rarely� is coded as a 3, �Only on holidays� is coded as a 2, and �Never� is coded as a 1.    5.	Income: The income variable is an ordinal variable recording the monthly take-home income range of the individual in New Israeli Shekels (Less than 2500 NIS, 2500-5000 NIS, 5000-7500 NIS, 7500-10000 NIS, 10000-12500 NIS, more than 15,000 NIS). The associated Q23 variable in the raw dataset is not recoded and appears as-is in the analysis dataset titled as income.  6.	Refugee: The refugee variable is a binary (0/1) record of whether the respondent is a registered refugee with UNRWA. In the raw data the refugee variable, Q24, is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset. 7.	Married: The Married variable is a binary (0/1) record of whether the respondent reports being married. The married variable is computed using the Q9 variable which is included in the raw dataset. In the raw dataset, if a respondent  the raw data the variable, Q9, is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.8.	Prison: The Prison variable is a binary (0/1) record of whether the respondent reports having spent time in an Israeli jail or prison. In the raw data the Prison variable, OV9, is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.9.	Party: The Party variable is a binary (0/1) record of whether the respondent reports being affiliated with a political party. In the raw data the Party variable, OV6, is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset. 10.	Al_fadi: The al_fadi variable is an ordinal variable recording the extent to which respondents agree with the statement, �Politics is meaningless and rarely benefits people like me� (Strongly Agree, Somewhat Agree, Somewhat Disagree, Strongly Disagree). In the analysis dataset I reversed the order of the associated numbers, such that a 4 is recorded when a respondent answers Strongly Agree, a 3 for Somewhat Agree, a 2 for Somewhat Disagree, and a 1 for Strongly disagree. When individuals declined to answer, what is recorded as a 5 in the raw dataset, this was recoded as a NA in the analysis dataset. 11.	Sex: The Sex variable is a binary (0/1) record of whether a respondent presents as male or female, coded by the survey enumerator. In the raw data the sex variable, Q1, is recorded using 1s (male) and 2s (female) and thus I converted all 2s to 0s in the analysis dataset. 12.	State_respon: State_respon is an ordinal variable recording the respondent�s level of satisfaction with the responsiveness of the municipality when a problem is identified and needs to be fixed (Satisfied/ Neutral/Not Satisfied). The associated State_respon variable in the raw dataset is not recoded and appears as-is in the analysis dataset titled as State_respon.13.	voting_norm_bi: The voting_norm_bi variable is a binary (0/1) record of whether an individual perceives voting to be an act of normalization and corresponds to the Q30a_1 variable in the raw dataset. In the raw data the Q30a_1 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset. 14.	medical_norm_bi: The medical_norm_bi variable is a binary (0/1) record of whether an individual perceives attending an Israeli hospital or medical clinic to be an act of normalization and corresponds to the Q30a_3 variable in the raw dataset. In the raw data the Q30a_3 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset. 15.	transport_norm_bi: The transport_norm_bi variable is a binary (0/1) record of whether an individual perceives using Israeli public transport to be an act of normalization and corresponds to the Q30a_5 variable in the raw dataset. In the raw data the Q30a_5 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset. 16.	police_norm_bi: The police_norm_bi variable is a binary (0/1) record of whether an individual perceives contacting the Israeli police to be an act of normalization and corresponds to the Q30a_4 variable in the raw dataset. In the raw data the Q30a_4 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.17.	edu_norm_bi: The edu_norm_bi variable is a binary (0/1) record of whether an individual perceives attending a Jerusalem Municipality Bagrut curriculum school to be an act of normalization and corresponds to the Q30a_6 variable in the raw dataset. In the raw data the Q30a_6 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.18.	cc_norm_bi: The cc_norm_bi variable is a binary (0/1) record of whether an individual perceives attending a Jerusalem Municipality Community Center to be an act of normalization and corresponds to the Q30a_8 variable in the raw dataset. In the raw data the Q30a_8 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.19.	sanitation_norm_bi: The sanitation_norm_bi variable is a binary (0/1) record of whether an individual perceives accessing Jerusalem Municipality sanitation services to be an act of normalization and corresponds to the Q30a_9 variable in the raw dataset. In the raw data the Q30a_9 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.20.	dispute_norm_bi: The dispute_norm_bi variable is a binary (0/1) record of whether an individual perceives settling a dispute with a friend or neighbor using the Israeli court system to be an act of normalization and corresponds to the Q30a_10 variable in the raw dataset. In the raw data the Q30a_10 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.21.	econ_assist_norm_bi:  The econ_assist_norm_bi variable is a binary (0/1) record of whether an individual perceives accepting economic relief or welfare from the Jerusalem Municipality or the Israeli National Insurance institute to be an act of normalization and corresponds to the Q30a_11 variable in the raw dataset. In the raw data the Q30a_11 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.22.	park_norm_bi: The park_norm_bi variable is a binary (0/1) record of whether an individual perceives going to and using a municipal park, soccer field, or playground to be an act of normalization and corresponds to the Q30a_12 variable in the raw dataset. In the raw data the Q30a_12 variable is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.23.	Police_station: The Police_station variable is a binary (1/0) record of whether an individual reports having visited a police station to report an issue or solve a problem.  In the raw data the Police_station variable, SC26_9, is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset. 24.	Edu_isr: The Edu_isr variable is a binary (0/1) record of whether the respondent went to a Jerusalem Municipality School. This variable is computed using variable SC7 from the raw dataset, and a 1 is recorded if the individual reports attending a Jerusalem municipality school (responses 2 or 3 in variable SC7).  25.	Isr.buses: Isr.buses is a binary (0/1) record of whether the respondent reports using Israeli buses. The associated Isr.buses variable in the raw dataset is not recoded and appears as-is in the analysis dataset titled as Isr.buses.26.	Isr.lightrail: Isr.lightrail is a binary (0/1) record of whether the respondent reports using the Israeli light rail. The associated Isr.lightrail variable in the raw dataset is not recoded and appears as-is in the analysis dataset titled as Isr.lightrail.27.	Housing: The Housing variable is a binary (0/1) record of whether the individual reports that they would contact the Israeli state if in need of a help with a problem related to housing. The Housing variable is computed as a composite of the following variables in the raw dataset: SC14_4, SC14_10, SC14_11, SC14_13. If an individual reports a 1 on any of these questions a 1 is recorded in the Housing variable. The responses to variables SC14_4, SC14_10, SC14_11, SC14_13 are not summed. 28.	Transport: The Transport variable is a binary (0/1) record of whether the respondent reports using Israeli state transportation options (buses and light rail) It is computed using the SC1_2 and SC1_3 variables in the raw dataset. If the SC1_2 or SC1_3 variables are recorded as a 1, the Transport variable is recorded as a 1. If the SC1_2 or SC1_3 variables are recorded as a 0, the Transport variable is recorded as a 0. 29.	Dispute: The Dispute variable is a binary (0/1) record of whether the individual reports that they would contact the Israeli state if in need of a help with a problem related to dispute resolution. The Dispute variable is computed as a composite of the following variables in the raw dataset: SC2_4, SC2_10, and SC2_11. If an individual reports a 1 on any of these questions a 1 is recorded in the Dispute variable. The responses to variables SC2_4, SC2_10, and SC2_11 are not summed.30.	Justice_crime: The Justice_crime variable is a binary (0/1) record of whether the individual reports that they would contact the Israeli state if in need of a help because they were accused of a crime. The Justice_crime variable is computed as a composite of the following variables in the raw dataset: SC3_4, SC3_10, and SC3_11. If an individual reports a 1 on any of these questions a 1 is recorded in the Justice_crime variable. The responses to variables SC3_4, SC3_10, and SC3_11 are not summed.31.	Justice_victim: The Justice_victim variable is a binary (0/1) record of whether the individual reports that they would contact the Israeli state if in need of a help because they were the victim of a crime. The Justice_victim variable is computed as a composite of the following variables in the raw dataset: SC5_4, SC5_10, and SC5_11. If an individual reports a 1 on any of these questions a 1 is recorded in the Justice_victim variable. The responses to variables SC5_4, SC5_10, and SC5_11 are not summed.32.	Police: The Police variable is a binary (0/1) record of whether the individual reports having called the police in the last five years. It is computed using the SC6 variable in the raw dataset. If the SC6 variable is recorded as a 2, 3, 4 the Police variable is recorded as a 1. If the SC6 variable is recorded as a 1, the Police variable is recorded as a 0. 33.	HU: The HU variable is a binary (0/1) record of whether the respondent reports that the Hebrew University is the best university option for East Jerusalemites (as compared to other Palestinian higher education options). It is computed using the SC8 variable in the raw dataset. If the SC8 variable is recorded as a 2, the HU variable is recorded as a 1. If the SC8 variable is recorded as a 1, 3, or 4 the HU variable is recorded as a 0. 34.	Clinic: The Clinic variable is a binary (0/1) record of whether the respondent reports that they would attend an Israeli medical clinic if in need of medical care. It is computed using the SC10 variable in the raw dataset. If the SC10 variable is recorded as a 1, the Clinic variable is recorded as a 1. If the SC10 variable is recorded as a 2 or 3, the Clinic variable is recorded as a 0. 35.	Hospital: The Hospital variable is a binary (0/1) record of whether the respondent reports that they would attend an Israeli hospital to give birth. It is computed using the SC11 variable in the raw dataset. If the SC11 variable is recorded as a 5 or 7, the Hospital variable is recorded as a 1. If the SC11 variable is recorded as a 1, 2, 3, 4, 6, or 8, the Hospital variable is recorded as a 0. 36.	Econ_assist: The Econ_assist variable is a binary (0/1) record of whether the respondent would seek social service assistance from Jerusalem municipality welfare offices or other social service providers within the municipality, or Israeli state social security or welfare offices. The Econ_assist variable is computed as a composite of the following variables in the raw dataset: SC13_4, SC13_10, and SC13_11. If an individual reports a 1 on any of these questions a 1 is recorded in the Econ_assist variable. The responses to variables SC13_4, SC13_10, and SC13_11 are not summed.37.	Water: The Water variable is a binary (0/1) record of whether the respondent would seek assistance from the Israeli state due to an issue related to drinking water. The Water variable is computed as a composite of the following variables in the raw dataset: SC15_4, SC15_10, SC15_11, and SC15_13. If an individual reports a 1 on any of these questions a 1 is recorded in the Water variable. The responses to variables SC15_4, SC15_10, SC15_11, and SC15_13 are not summed.38.	Bagrut: The bagrut variable is a binary (0/1) record of whether the respondent went to a Jerusalem Municipality school which uses the bagrut curriculum. This variable is computed using variable SC7 from the raw dataset, and a 1 is recorded if the individual reports attending a Jerusalem municipality school using the bagrut curriculum (response 2 in variable SC7).  39.	Edu_child: The edu_child variable is a binary (0/1) record of whether the respondent�s child went to a Jerusalem Municipality school which uses the bagrut curriculum. This variable is computed using variable SC7_1 from the raw dataset, and a 1 is recorded if the individual reports that their child attends/attended a Jerusalem municipality school using the bagrut curriculum (response 2 in variable SC7_1).  40.	Sanitation: The Sanitation variable is a binary (0/1) record of whether the respondent would seek assistance from the Israeli state due to an issue related to sanitation in their neighborhood. The Sanitation variable is computed as a composite of the following variables in the raw dataset: SC16_4, SC16_10, SC16_11, and SC16_13. If an individual reports a 1 on any of these questions a 1 is recorded in the Sanitation variable. The responses to variables SC16_4, SC16_10, SC16_11, and SC16_13 are not summed.41.	Infrastructure: The Infrastructure variable is a binary (0/1) record of whether the respondent would seek assistance from the Israeli state due to an issue related to infrastructure in their neighborhood. The Infrastructure variable is computed as a composite of the following variables in the raw dataset: SC17_4, SC17_10, SC17_11, and SC17_13. If an individual reports a 1 on any of these questions a 1 is recorded in the Infrastructure variable. The responses to variables SC17_4, SC17_10, SC17_11, and SC17_13 are not summed.42.	Electricity: The Electricity variable is a binary (0/1) record of whether the respondent would seek assistance from the Israeli state due to an issue related to electricity in their neighborhood. The Electricity variable is computed as a composite of the following variables in the raw dataset: SC18_4, SC18_10, SC18_11, and SC18_13. If an individual reports a 1 on any of these questions a 1 is recorded in the Electricity variable. The responses to variables SC18_4, SC18_10, SC18_11, and SC18_13 are not summed.43.	Comm_Center: The Comm_center variable is a binary (0/1) record of whether the respondent reports having ever visited a municipal community center. The comm_center variable is computed using the corresponding SC20 variable in the raw dataset. If respondents reported having visited a community one or more times (responses 1, 2, 3) the response is recorded as a 1 in the comm_center variable. If the respondent reports having never been to a community center (response 4 in raw dataset variable SC20) the response is recorded as a 0 in the comm_center variable in the analysis dataset. 44.	Parks: The Parks variable is a binary (0/1) record of whether the respondent reports that they would make use of a newly constructed Israeli park/playground/community gardens/soccer field in their neighborhood. It is computed using the SC22 variable in the raw dataset. If the SC22 variable is recorded as a 1 or a 2, the Parks variable is recorded as a 1. If the SC22 variable is recorded as a 3, the Parks variable is recorded as a 0. 45.	Voting: The variable Voting is a binary (0/1) record of whether the respondent has ever voted in a municipal election. In the raw data the corresponding variable, SC25, is recorded using 1s (yes) and 2s (no) and thus I converted all 2s to 0s in the analysis dataset.  